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   "source": [
    "# 卷积神经网络的一些知识\n",
    "\n",
    "卷积神经网络的操作：\n",
    "* 使用过滤器进行卷积操作，本质为将过滤矩阵与kernel进行内积。此外通常使用多个过滤器对多个特征进行过滤。输入对象的通道数量与过滤器的通道数量一致。\n",
    "* 重要的参数：1、过滤器的大小的设置，即kernel_size的设置。2、移动步长的设置。3、是否需要填补，即当你的kernel在移动之后超出范围后，是否需要用数字填充。\n",
    "* 池化操作：下采样操作，清晰的图像在减少部分列和部分行后不会改变原图像的特征。"
   ]
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  {
   "cell_type": "markdown",
   "id": "bad8b963-fff5-4050-a39e-08fde68d8295",
   "metadata": {},
   "source": [
    "# 范式\n",
    "卷积+池化+卷积+池化+$\\cdots$+全连接神经网络"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bfa2d40-36c9-47fa-a86b-62fd9a6b44f3",
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   "cell_type": "markdown",
   "id": "d8ba3bde-5ffe-47f1-9f40-1bb2c0471ba5",
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   "source": [
    "# 卷积神经网络的输出的大小:\n",
    "影响输出层的大小的参数有:\n",
    "* 输入的大小:N$\\times$N\n",
    "* kernel的大小:K$\\times$K\n",
    "* 步长：S(stride)\n",
    "* 填充：P(padding)\n",
    "\n",
    "$$\n",
    "OutputSize = \\ [\\frac{N+2P-K}{S}\\ ] + 1\n",
    "$$"
   ]
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